The goal of this project is to use experiments and computational simulations to determine whether different rates of P450 catalyzed N-oxidation, the first step in the mutagenic pathway, are a major factor in modulating the potency of 2-aminoimidazole-azaarene (AIA) food mutagens. These compounds share a common, mutagenically active 2- aminoimidazole group; however, they exhibit a 100-million fold range of mutagenic potency. The mechanisms underlying this wide distribution in activity are not known, although quantitative structure activity relationships (QSARs) and preliminary experimental data indicate that at least some of the variation in mutagenic potency results from different rates of P450 metabolic activation. Identifying the factors that modulate mutagenic potency will give us a means to better predict the human health risks associated with exposure to these food mutagens. This project requires determining the N-oxidation mechanism and active site structure of the human CYP1A2 isoform of cytochrome P450. We will use homology-based protein modeling methods to develop a structural model of the CYP1A2 enzyme active site. Although crystallographic structures are not yet available for any CYP1A2 isoforms, several studies have shown that effective models of these enzymes can be developed based on bacterial P450 structures. Moreover, a crystal structure of a mammalian isoform, rabbit CYP2C5 has just been released which we will use as an additional modeling template. Quantum chemical simulations will be used to predict the energies of proposed oxidation intermediates and chemical properties for QSAR analysis. Concurrent with the simulations, we will generate a consistent set of AIA CYP1A2 oxidation kinetics and Ames mutagenicity data. Together, this data will be used to determine the role of phase I oxidation in the mutagenicity of AIA food mutagens, and to develop a mechanism-based QSAR to explain their varying potencies. Our aims reflect recent changes in the focus of environmental carcinogen research. In addition to predicting the endpoints themselves, toxicological research now seeks to understand the mechanistic principles underlying biological response. Our project addresses this goal by integrating traditional toxicology data (experimental mutagenicity and metabolism measurements) with protein structure and sequence data using computational simulations.